Novel quality features to expand durum wheat applications.

Durum wheat represents a staple food in the human diet owing to its nutritional and technological features. In comparison to common wheat, durum wheat has higher tolerance to biotic and abiotic stresses. However, its production and culinary uses are limited compared to common wheat. Therefore, significant attention was attributed to upgrading the key quality of durum wheat (i.e., hardness, protein, starch and color). This review intends to put the spotlight on the modification of these properties to create new functionalities suiting a wider range of food applications based on critical compilation of scientific publications. Targeting specific genes has been shown to be a valuable strategy to design novel wheat varieties with higher nutritional value (e.g., high amylose), improved technological properties (e.g., higher glutenin content), attractive appearance (e.g., colored wheat) and new uses (e.g., soft durum wheat for breadmaking). Further efforts are still needed to find efficient ways to stabilize and maintain these properties. © 2022 Society of Chemical Industry.

Genetic architecture of end-use quality traits in soft white winter wheat.

BACKGROUND: Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States. RESULTS: This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end-use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection. CONCLUSIONS: In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on.

Genome-wide association mapping of the 'super-soft' kernel texture in white winter wheat.

KEY MESSAGE: The novel super-soft kernel phenotype has the potential to improve wheat processing and flour quality. We identified genomic regions associated with this kernel texture in white winter wheat. Grain hardness is a key determinant of wheat milling and baking quality. The recently discovered 'super-soft' kernel phenotype has the potential to improve wheat processing and flour quality. However, the genetic basis underlying the super-soft trait in wheat is not yet well understood. In this study, we investigated the phenotypic and genotypic structure of the super-soft trait in a collection of 172 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the USA. This collection had a continuous distribution for grain hardness index (single-kernel characterization system). Ten super-soft genotypes showed hardness index ≤ 12 including the cultivar Jasper. Over 98,000 SNP markers from genotyping-by-sequencing were used for association mapping (GWAS). The GWAS identified 20 significant markers associated with grain hardness. These significant SNPs corresponded to seven QTL on chromosomes 2B, 3A, 3B, 5A, 6B,7A, and one unaligned chromosome. Two of these QTL, QSKhard.wql-3A and QSKhard.wql-5A, had large effects and distinguished between the normal soft and the super-soft classes. QSKhard.wql-3A and QSKhard.wql-5A reduced the hardness index by 11.7 and 13.1 on average, respectively. The remaining QTL had small effects and reduced grain hardness within the normal soft range. QSKhard.wql-2B, QSKhard.wql-3A, QSKhard.wql-3B, and QSKhard.wql-6B were not previously reported to be in genomic regions of grain hardness-related genes/QTL. The identified super-soft genotypes as well as the SNPs associated with lower grain hardness will be useful to assist breeding for this grain texture trait.

Genotyping-by-Sequencing for the Study of Genetic Diversity in Puccinia triticina.

Leaf rust, caused by Puccinia triticina Erikss., is globally the most widespread rust of wheat. Populations of P. triticina are highly diverse for virulence, with many different races found annually. The genetic diversity of P. triticina populations has been previously assessed using different types of DNA markers. Genotyping technologies that provide a higher density of markers distributed across the genome will be more powerful for analysis of genetic and phylogenetic relationships in P. triticina populations. In this study, we utilized restriction-associated DNA (RAD) genotyping-by-sequencing (GBS) adapted for the Ion Torrent sequencing platform for the study of population diversity in P. triticina. A collection of 102 isolates, collected mainly from tetraploid and hexaploid wheat, was used. The virulence phenotypes of the isolates were determined on 20 lines of Thatcher wheat near isogenic for leaf rust resistance genes. Seven races were found among 57 isolates collected from tetraploid wheat, and 21 races were observed among 40 hexaploid wheat type isolates. This is the first study to report durum wheat virulent races to Lr3bg in Tunisia, Lr14a in Morocco, and Lr3bg and Lr28 in Mexico. Ethiopian isolates with high virulence to durum wheat but avirulent on Thatcher (hexaploid wheat) were tested for virulence on a set of durum (tetraploid) differentials. A subset of 30 isolates representing most of the virulence phenotypes in the 102 isolates were genotyped using RAD-GBS. Phylogenetic analysis of 30 isolates using 2,125 single nucleotide polymorphism (SNP) markers showed nine distinct clusters. There was a general correlation between virulence phenotypes and SNP genotypes. The high bootstrap values between clusters of isolates in the phylogenetic tree indicated that RAD-GBS can be used as a new genotyping tool that is fast, simple, high throughput, cost effective, and provides a sufficient number of markers for the study of genetic diversity in P. triticina.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Low-cost grain sorting technologies to reduce mycotoxin contamination in maize and groundnut.

The widespread contamination of foods by mycotoxins continues to be a public health hazard in sub-Saharan Africa, with maize and groundnut being major sources of contamination. This study was undertaken to assess the hypothesis that grain sorting can be used to reduce mycotoxin contamination in grain lots by removing toxic kernels. We tested a set of sorting principles and methods for reducing mycotoxin levels in maize and groundnut from a variety of genotypes and environments. We found that kernel bulk density (KBD) and 100-kernel weight (HKW) were associated with the levels of aflatoxins (AF) and fumonisins (FUM) in maize grain. A low-cost sorter prototype (the 'DropSort' device) that separated maize grain based on KBD and HKW was more effective in reducing FUM than AF. We then evaluated the effectiveness of DropSorting when combined with either size or visual sorting. Size sorting followed by DropSorting was the fastest method for reducing FUM to under 2 ppm, but was not effective in reducing AF levels in maize grain to under 20 ppb, especially for heavily AF-contaminated grain. Analysis of individual kernels showed that high -AF maize kernels had lower weight, volume, density, length, and width and higher sphericity than those with low AF. Single kernel weight was the most significant predictor of AF concentration. The DropSort excluded kernels with lower single kernel weight, volume, width, depth, and sphericity. We also found that visual sorting and bright greenish-yellow fluorescence sorting of maize single kernels were successful in separating kernels based on AF levels. For groundnut, the DropSort grouped grain based on HKW and did not significantly reduce AF concentrations, whereas size sorting and visual sorting were much more effective.

Dissection of the multigenic wheat stem rust resistance present in the Montenegrin spring wheat accession PI 362698.

BACKGROUND: Research to identify and characterize stem rust resistance genes in common wheat, Triticum aestivum, has been stimulated by the emergence of Ug99-lineage races of the wheat stem rust pathogen, Puccinia graminis f. sp. tritici (Pgt), in Eastern Africa. The Montenegrin spring wheat landrace PI 362698 was identified as a source of Pgt resistance. This accession exhibits resistance to multiple Ug99-lineage and North American Pgt races at seedling and adult-plant stages. A recombinant inbred population was developed by crossing the susceptible line LMPG-6 with a single plant selection of PI 362698. A genetic map was constructed using the Illumina iSelect 90 K wheat assay and the markers csLv34, NB-LRR3, and wMAS000003 and quantitative trait locus (QTL) analysis was performed. RESULTS: QTL analysis identified five significant QTLs (α = 0.05) on chromosomes 2B, 3B, 6A, 6D, and 7A associated with wheat stem rust resistance. The QTL on chromosome 3B was identified using both field data from Kenya (Pgt Ug99-lineage races) and seedling data from Pgt race MCCF. This QTL potentially corresponds to Sr12 or a new allele of Sr12. The multi-pathogen resistance gene Sr57 located on chromosome 7D is present in PI 362698 according to the diagnostic markers csLv34 and wMAS000003, however a significant QTL was not detected at this locus. The QTLs on chromosomes 2B, 6A, and 6D were identified during seedling trials and are thought to correspond to Sr16, Sr8a, and Sr5, respectively. The QTL identified on chromosome 7A was detected using MCCF seedling data and may be Sr15 or a potentially novel allele of recently detected Ug99 resistance QTLs. CONCLUSIONS: The combination of resistance QTLs found in PI 362698 is like the resistance gene combination present in the broadly resistant cultivar Thatcher. As such, PI 362698 may not be a landrace as previously thought. PI 362698 has been crossed with North Dakota wheat germplasm for future breeding efforts. Additional work is needed to fully understand why the combination of genes present in PI 362698 and 'Thatcher' provide such durable resistance.

Inheritance and Bulked Segregant Analysis of Leaf Rust and Stem Rust Resistance in Durum Wheat Genotypes.

Leaf rust, caused by Puccinia triticina, and stem rust, caused by P. graminis f. sp. tritici, are important diseases of durum wheat. This study determined the inheritance and genomic locations of leaf rust resistance (Lr) genes to P. triticina race BBBQJ and stem rust resistance (Sr) genes to P. graminis f. sp. tritici race TTKSK in durum accessions. Eight leaf-rust-resistant genotypes were used to develop biparental populations. Accessions PI 192051 and PI 534304 were also resistant to P. graminis f. sp. tritici race TTKSK. The resulting progenies were phenotyped for leaf rust and stem rust response at seedling stage. The Lr and Sr genes were mapped in five populations using single-nucleotide polymorphisms and bulked segregant analysis. Five leaf-rust-resistant genotypes carried single dominant Lr genes whereas, in the remaining accessions, there was deviation from the expected segregation ratio of a single dominant Lr gene. Seven genotypes carried Lr genes different from those previously characterized in durum. The single dominant Lr genes in PI 209274, PI 244061, PI387263, and PI 313096 were mapped to chromosome arms 6BS, 2BS, 6BL, and 6BS, respectively. The Sr gene in PI 534304 mapped to 6AL and is most likely Sr13, while the Sr gene in PI 192051 could be uncharacterized in durum.

Quantitative proteomic analysis of super soft kernel texture in soft white spring wheat.

Super soft kernel texture is associated with superior milling and baking performance in soft wheat. To understand the mechanism underlying super soft kernel texture, we studied proteomic changes between a normal soft and a super soft during kernel development. The cultivar 'Alpowa', a soft white spring wheat, was crossed to a closely related super soft spring wheat line 'BC2SS163' to produce F6 recombinant inbred lines (RILs). Four normal soft RILs and four super soft RILs along with the parents were selected for proteomic analysis. Alpowa and the normal soft RILs showed hardness indices of 20 to 30, whereas BC2SS163 and the super soft RILs showed hardness indices of -2 to -6. Kernels were collected from normal soft and super soft genotypes at 7 days post anthesis (dpa), 14 dpa, 28 dpa, and maturity and were subject to quantitative proteomic analysis. Throughout kernel development, 175 differentially abundant proteins (DAPs) were identified. Most DAPs were observed at 7 dpa, 14 dpa, and 28 dpa. Of the 175 DAPs, 32 had higher abundance in normal soft wheat, whereas 143 DAPs had higher abundance in super soft wheat. A total of 18 DAPs were associated with carbohydrate metabolism and five DAPs were associated with lipids. The gene TraesCS4B02G091100.1 on chromosome arm 4BS, which encodes for sucrose-phosphate synthase, was identified as a candidate gene for super soft kernel texture in BC2SS163. This study enhanced our understanding of the mechanism underlying super soft kernel texture in soft white spring wheat.

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat.

Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait-based GS models. This study's main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait- and multi-trait-based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait-based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models.

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year's dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.

Novel stripe rust all-stage resistance loci identified in a worldwide collection of durum wheat using genome-wide association mapping.

Durumwheat [Triticum turgidum L. ssp. durum (Desf.)] production is constrained by fungal diseases including stripe rust caused by Puccinia striiformis Westend. f. sp. tritici Erikss. (Pst). Continuous mining of germplasm for the discovery and deployment of stripe rust resistance (Yr) genes is needed to counter the impact of this disease. In this study, we evaluated a worldwide collection of 432 durum wheat accessions to seven U.S. Pst races that carry diverse virulence and avirulence combinations on wheat Yr genes. We found that 47-82% of the durum wheat accessions were susceptible to each of the tested Pst races. A total of 32 accessions were resistant to all seven races. Genome-wide association studies (GWAS) using over 97,000 single-nucleotide polymorphism markers generated from genotyping-by-sequencing of 364 accessions identified 56 quantitative trait loci (QTL) associated with all-stage stripe rust resistance located on all 14 durum wheat chromosomes. Six of these QTL were associated with resistance to 2-4 Pst races, and none were associated with resistance to all seven races. The remaining 50 QTL were race specific. Eighteen of the 56 identified QTL had relatively large effects against at least one of the races. A map-based comparison of the discovered QTL in this study with previously published Yr genes and QTL showed that 29 were previously identified, whereas the remaining 27 QTL appeared to be novel. This study reports effective sources of stripe rust resistance to contemporary races in the United States and shows that this durum wheat collection is abundant in novel resistance loci that can be transferred into adapted durum cultivars.

Environment characterization and genomic prediction for end-use quality traits in soft white winter wheat.

End-use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end-use quality traits in soft white wheat (Triticum aestivum L.) and tested the effectiveness of implementing genomic selection to optimize breeding for these traits. We used a multi-environment unbalanced dataset comprised of 672 breeding lines and cultivars adapted to the Pacific Northwest region of the United States, which were evaluated for 14 end-use quality traits. Genetic correlations between environments based on factor analytic models showed low-to-moderate G × E interaction for most traits but high G × E interaction for grain and flour protein. A total of 40,518 single-nucleotide polymorphism markers were used for genomic prediction. Genomic prediction accuracies were high for most traits thereby justifying the use of genomic selection to assist breeding for superior end-use quality in soft white wheat. Excluding outlier environments based on genetic correlations between environments was more effective in increasing genomic prediction accuracies compared with that based on environment clustering analysis. For kernel size, kernel weight, milling score, ash, and flour swelling volume, excluding outlier environments increased prediction accuracies by 1-11%. However, for grain and flour protein, flour yield, and cookie diameter, excluding outlier environments did not improve genomic prediction performance.

Genome-Wide Association Studies Reveal All-Stage Rust Resistance Loci in Elite Durum Wheat Genotypes.

Leaf rust, caused by Puccinia triticina (Pt), stripe rust caused by Puccinia striiformis f. sp. tritici (Pst), and stem rust caused by Puccinia graminis f. sp. tritici (Pgt) are major diseases to wheat production globally. Host resistance is the most suitable approach to manage these fungal pathogens. We investigated the phenotypic and genotypic structure of resistance to leaf rust, stem rust, and stripe rust pathogen races at the seedling stage in a collection of advanced durum wheat breeding lines and cultivars adapted to Upper Mid-West region of the United States. Phenotypic evaluation showed that the majority of the durum wheat genotypes were susceptible to Pt isolates adapted to durum wheat, whereas all the genotypes were resistant to common wheat type-Pt isolate. The majority of genotypes were resistant to stripe rust and stem rust pathogen races. The durum panel genotyped using Illumina iSelect 90 K wheat SNP assay was used for genome-wide association mapping (GWAS). The GWAS revealed 64 marker-trait associations (MTAs) representing six leaf rust resistance loci located on chromosome arms 2AS, 2AL, 5BS, 6AL, and 6BL. Two of these loci were identified at the positions of Lr52 and Lr64 genes, whereas the remaining loci are most likely novel. A total of 46 MTAs corresponding to four loci located on chromosome arms 1BS, 5BL, and 7BL were associated with stripe rust response. None of these loci correspond to designated stripe rust resistance genes. For stem rust, a total of 260 MTAs, representing 22 loci were identified on chromosome arms 1BL, 2BL, 3AL, 3BL, 4AL, 5AL, 5BL, 6AS, 6AL, 6BL, and 7BL. Four of these loci were located at the positions of known genes/alleles (Sr7b, Sr8155B1, Sr13a, and Sr13b). The discovery of known and novel rust resistance genes and their linked SNPs will help diversify rust resistance in durum wheat.

Genome-Wide Association Study of a Worldwide Collection of Wheat Genotypes Reveals Novel Quantitative Trait Loci for Leaf Rust Resistance.

CORE IDEAS: The emergence of new virulent Puccinia triticina races requires a continuous search for novel sources of resistance to combat leaf rust (LR) disease Twenty-two wheat genotypes resistant to four P. triticina races were identified in this study A genome-wide association study detected 11 quantitative trait loci for LR resistance; five of them were detected on genomic regions where no LR resistant genes have been detected. Wheat (Triticum aestivum L.) production worldwide is being challenged by several biotic and abiotic factors. Leaf rust (LR), caused by Puccinia triticina, is a major biotic constraint of wheat production worldwide. Genetic resistance is the most efficient and cost-effective way to control LR. Seventy-nine LR resistance genes have been identified to date but the frequent emergence of new virulent P. triticina races every year demands a constant search for new sources of resistance with novel quantitative trait loci (QTL) or genes. The objectives of this study were to identify putative novel sources of effective resistance against the current prevalent races of P. triticina in the southeast United States and to map genomic loci associated with LR resistance via a genome-wide association study (GWAS) approach. Evaluation of 331 diverse wheat genotypes against four prevalent P. triticina races (MFGKG, MBTNB, MCTNB, and TCRKG) revealed that the majority of the genotypes were susceptible and only 22 genotypes (6.6%) were resistant to all four P. triticina races. The GWAS detected 11 QTL on nine chromosomes for LR resistance. Of these, six QTL were identified in the vicinity of known genes or QTL; therefore, more studies are warranted to determine their relationship. Five QTL (QLr.uga-1AL, QLr.uga-4AS, QLu.uga-5AS, QLr.uga-5AL, and QLr.uga-7AS) were identified on genomic regions where no LR resistance genes have been identified in wheat, representing potential novel loci for LR resistance. The highly resistant wheat genotypes and novel QTL reported in this study could be used in breeding programs to improve LR resistance.

Association Mapping for 24 Traits Related to Protein Content, Gluten Strength, Color, Cooking, and Milling Quality Using Balanced and Unbalanced Data in Durum Wheat [Triticum turgidum L. var. durum (Desf).].

Durum wheat [Triticum durum (Desf).] is mostly used to produce pasta, couscous, and bulgur. The quality of the grain and end-use products determine its market value. However, quality tests are highly resource intensive and almost impossible to conduct in the early generations in the breeding program. Modern genomics-based tools provide an excellent opportunity to genetically dissect complex quality traits to expedite cultivar development using molecular breeding approaches. This study used a panel of 243 cultivars and advanced breeding lines developed during the last 20 years to identify SNPs associated with 24 traits related to nutritional value and quality. Genome-wide association study (GWAS) identified a total of 179 marker-trait associations (MTAs), located in 95 genomic regions belonging to all 14 durum wheat chromosomes. Major and stable QTLs were identified for gluten strength on chromosomes 1A and 1B, and for PPO activity on chromosomes 1A, 2B, 3A, and 3B. As a large amount of unbalance phenotypic data are generated every year on advanced lines in all the breeding programs, the applicability of such a dataset for identification of MTAs remains unclear. We observed that ∼84% of the MTAs identified using a historic unbalanced dataset (belonging to a total of 80 environments collected over a period of 16 years) were also identified in a balanced dataset. This suggests the suitability of historic unbalanced phenotypic data to identify beneficial MTAs to facilitate local-knowledge-based breeding. In addition to providing extensive knowledge about the genetics of quality traits, association mapping identified several candidate markers to assist durum wheat quality improvement through molecular breeding. The molecular markers associated with important traits could be extremely useful in the development of improved quality durum wheat cultivars using marker-assisted selection (MAS).

Mapping of Novel Leaf Rust and Stem Rust Resistance Genes in the Portuguese Durum Wheat Landrace PI 192051.

Leaf rust caused by Puccinia triticina Erikss. (Pt) and stem rust caused by Puccinia graminis f. sp. tritici Erikss. & E. Henn (Pgt) are serious constraints to production of durum wheat (Triticum turgidum L). The objective of this study was to identify leaf rust resistance (Lr) and stem rust resistance (Sr) genes/QTL in Portuguese durum landrace PI 192051. Four Pt-isolates, representing three virulence phenotypes (BBBQJ, BBBSJ & EEEEE) and six Pgt-races TTKSK, JRCQC, TKTTF, QFCFC, TPMKC and TMLKC were used to evaluate 180 recombinant inbred lines (RILs) derived from the cross Rusty (rust susceptible) × PI 192051-1 (rust resistant) at the seedling stage. The RILs were also phenotyped at the adult-plant stage in a stem rust nursery in Ethiopia in 2017. The RILs were genotyped using the Illumina iSelect 9K wheat SNP array. PI 192051-1 carries a previously unidentified major Sr gene designated as QSr.ace-7A on chromosome arm 7AS and Lr gene Lr.ace-4A in the pericentromeric region of chromosome 4A. In addition, three minor Sr QTL QSr.ace-1A, QSr.ace-2B and QSr.ace-4A were mapped in PI 192051-1 on chromosomes 1AL, 2BL, and 4A, respectively Lr.ace-4A could be co-located or tightly linked to QSr.ace-4A Markers linked to the identified QTL/genes can be used for marker assisted selection. These findings enrich the genetic basis of rust resistance in both durum and common wheat.

Genome-Wide Association Mapping of Leaf Rust Response in a Durum Wheat Worldwide Germplasm Collection.

Leaf rust (caused by Erikss. []) is increasingly impacting durum wheat ( L. var. ) production with the recent appearance of races with virulence to widely grown cultivars in many durum producing areas worldwide. A highly virulent race on durum wheat was recently detected in Kansas. This race may spread to the northern Great Plains, where most of the US durum wheat is produced. The objective of this study was to identify sources of resistance to several races from the United States and Mexico at seedling stage in the greenhouse and at adult stage in field experiments. Genome-wide association study (GWAS) was used to identify single-nucleotide polymorphism (SNP) markers associated with leaf rust response in a worldwide durum wheat collection of 496 accessions. Thirteen accessions were resistant across all experiments. Association mapping revealed 88 significant SNPs associated with leaf rust response. Of these, 33 SNPs were located on chromosomes 2A and 2B, and 55 SNPs were distributed across all other chromosomes except for 1B and 7B. Twenty markers were associated with leaf rust response at seedling stage, while 68 markers were associated with leaf rust response at adult plant stage. The current study identified a total of 14 previously uncharacterized loci associated with leaf rust response in durum wheat. The discovery of these loci through association mapping (AM) is a significant step in identifying useful sources of resistance that can be used to broaden the relatively narrow leaf rust resistance spectrum in durum wheat germplasm.